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Charles Isbell,
Michael Littman,
and
Pushkar Kolhe
Take Udacity's free Unsupervised Learning course and learn how you can use Unsupervised Learning approaches to find structure in unlabeled data. Learn online with Udacity.
Register for this course and see more details by visiting:
OpenCourser.com/course/dnibbe/udacity
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What's inside
Syllabus
UL 1 - Randomized Optimization
UL 2 - Clustering
UL 3 - Feature Selection
UL 4 - Feature Transformation
Read more
Syllabus
UL 1 - Randomized Optimization
UL 2 - Clustering
UL 3 - Feature Selection
UL 4 - Feature Transformation
Read more
UL 5 - Info Theory
Final Project for Udacity Students
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores unsupervised learning, which is increasingly relevant to data mining and machine learning
Led by experienced instructors Charles Isbell, Michael Littman, and Pushkar Kolhe
Offers hands-on labs and interactive materials
Taught by Charles Isbell, who developed the first known robot to learn to walk with reinforcement learning
Taught by Michael Littman, who developed the first known computer agent which mastered game of Go
Provides a strong foundation for beginners in unsupervised learning
Save this course
Save Machine Learning: Unsupervised Learning to your list so you can find it easily later:
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Reviews summary
Ml: unsupervised learning concepts and application
Machine Learning: Unsupervised Learning teaches you the concepts and applications of unsupervised learning, a powerful tool for finding structure in data. The course is well-received, with students praising the engaging teaching style of the two professors and the hands-on final project, where students build a movie recommendation system like Netflix.
Course includes a hands-on project.
"...experience implementing them in this course through a hands-on final project in which you will be designing a movie recommendation system (just like Netflix!)."
Instructors are engaging and knowledgeable.
"The way in which the instructors teach is awesome."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Machine Learning: Unsupervised Learning with these
activities:
Review Introduction to Machine Learning Algorithms
Show steps
Reinforce your understanding of the basic concepts of machine learning.
Browse courses on
Machine Learning Algorithms
Show steps
-
Re-read your notes from any previous courses on machine learning algorithms.
-
Review materials from online resources, such as tutorials or blog posts.
-
Complete practice problems or exercises related to machine learning algorithms.
Read 'Unsupervised Learning' by Hastie, Tibshirani, and Friedman
Show steps
Gain a deeper understanding of unsupervised learning concepts from a comprehensive textbook.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
-
Obtain a copy of the book.
-
Read the chapters relevant to the course topics.
-
Take notes and highlight important concepts.
-
Complete the exercises and assignments included in the book.
Follow Guided Tutorials on k-Means Clustering
Show steps
Deepen your understanding of k-Means clustering.
Browse courses on
K-Means Clustering
Show steps
-
Find online tutorials that provide a step-by-step guide to k-Means clustering.
-
Follow the instructions in the tutorials to implement k-Means clustering on a dataset.
-
Experiment with different values of k to observe how they affect the clustering results.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend an Industry Conference on Unsupervised Learning
Show steps
Connect with professionals in the field and learn about the latest advancements in unsupervised learning.
Browse courses on
Unsupervised Learning
Show steps
-
Research and identify relevant industry conferences.
-
Register for the conference and make arrangements for travel and accommodation.
-
Attend the conference sessions, workshops, and networking events.
-
Follow up with new connections made at the conference.
Complete Practice Problems on Feature Selection
Show steps
Strengthen your ability to apply feature selection techniques.
Browse courses on
Feature Selection
Show steps
-
Find online resources or textbooks that offer practice problems on feature selection.
-
Solve the practice problems to gain hands-on experience in selecting informative features.
-
Analyze the results of your feature selection and identify patterns.
Volunteer at a Data Science Hackathon
Show steps
Gain practical experience in unsupervised learning by working on a team project.
Browse courses on
Data Science
Show steps
-
Find a data science hackathon that aligns with your interests.
-
Form a team or join an existing team.
-
Contribute to the team's project by applying unsupervised learning techniques.
-
Present your team's project to the hackathon judges.
Create a Presentation on Info Theory
Show steps
Enhance your understanding of information theory by explaining it to others.
Browse courses on
Information Theory
Show steps
-
Gather information and resources on information theory.
-
Organize your content into a logical flow.
-
Design slides that are clear and visually appealing.
-
Practice presenting your information to an audience.
Contribute to an Open-Source Unsupervised Learning Project
Show steps
Gain hands-on experience and contribute to the unsupervised learning community.
Browse courses on
Open Source
Show steps
-
Identify open-source unsupervised learning projects on platforms like GitHub.
-
Choose a project that aligns with your interests and skills.
-
Fork the project and make a copy on your own GitHub account.
-
Make changes and contribute to the project.
Review Introduction to Machine Learning Algorithms
Show steps
Reinforce your understanding of the basic concepts of machine learning.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Re-read your notes from any previous courses on machine learning algorithms.
- Review materials from online resources, such as tutorials or blog posts.
- Complete practice problems or exercises related to machine learning algorithms.
Read 'Unsupervised Learning' by Hastie, Tibshirani, and Friedman
Show steps
Gain a deeper understanding of unsupervised learning concepts from a comprehensive textbook.
View
The Elements of Statistical Learning: Data...
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of the book.
- Read the chapters relevant to the course topics.
- Take notes and highlight important concepts.
- Complete the exercises and assignments included in the book.
Follow Guided Tutorials on k-Means Clustering
Show steps
Deepen your understanding of k-Means clustering.
Browse courses on
K-Means Clustering
Show steps
Show steps
Show steps
- Find online tutorials that provide a step-by-step guide to k-Means clustering.
- Follow the instructions in the tutorials to implement k-Means clustering on a dataset.
- Experiment with different values of k to observe how they affect the clustering results.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend an Industry Conference on Unsupervised Learning
Show steps
Connect with professionals in the field and learn about the latest advancements in unsupervised learning.
Browse courses on
Unsupervised Learning
Show steps
Show steps
Show steps
- Research and identify relevant industry conferences.
- Register for the conference and make arrangements for travel and accommodation.
- Attend the conference sessions, workshops, and networking events.
- Follow up with new connections made at the conference.
Complete Practice Problems on Feature Selection
Show steps
Strengthen your ability to apply feature selection techniques.
Browse courses on
Feature Selection
Show steps
Show steps
Show steps
- Find online resources or textbooks that offer practice problems on feature selection.
- Solve the practice problems to gain hands-on experience in selecting informative features.
- Analyze the results of your feature selection and identify patterns.
Volunteer at a Data Science Hackathon
Show steps
Gain practical experience in unsupervised learning by working on a team project.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find a data science hackathon that aligns with your interests.
- Form a team or join an existing team.
- Contribute to the team's project by applying unsupervised learning techniques.
- Present your team's project to the hackathon judges.
Create a Presentation on Info Theory
Show steps
Enhance your understanding of information theory by explaining it to others.
Browse courses on
Information Theory
Show steps
Show steps
Show steps
- Gather information and resources on information theory.
- Organize your content into a logical flow.
- Design slides that are clear and visually appealing.
- Practice presenting your information to an audience.
Contribute to an Open-Source Unsupervised Learning Project
Show steps
Gain hands-on experience and contribute to the unsupervised learning community.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open-source unsupervised learning projects on platforms like GitHub.
- Choose a project that aligns with your interests and skills.
- Fork the project and make a copy on your own GitHub account.
- Make changes and contribute to the project.
Career center
Learners who complete Machine Learning: Unsupervised Learning will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst gathers and interprets data to help organizations make informed decisions. This course can help build a foundation for a career as a Data Analyst by providing a strong understanding of unsupervised learning techniques. Unsupervised learning is a powerful tool for finding patterns and insights in unlabeled data, which is often the type of data that Data Analysts work with.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. This course can help build a foundation for a career as a Machine Learning Engineer by providing a strong understanding of unsupervised learning techniques. Unsupervised learning is a powerful tool for finding patterns and insights in unlabeled data, which is often the type of data that Machine Learning Engineers work with.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. This course can help build a foundation for a career as a Data Scientist by providing a strong understanding of unsupervised learning techniques. Unsupervised learning is a powerful tool for finding patterns and insights in unlabeled data, which is often the type of data that Data Scientists work with.
Research Scientist
Research Scientist
A Research Scientist conducts research to advance scientific knowledge. This course may be useful for a Research Scientist who is interested in using unsupervised learning techniques to find patterns and insights in unlabeled data. Unsupervised learning is a powerful tool for exploring new data and generating hypotheses.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course may be useful for a Software Engineer who is interested in using unsupervised learning techniques to improve the performance of software applications. Unsupervised learning is a powerful tool for finding patterns and insights in data, which can be used to improve the efficiency and accuracy of software applications.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. This course may be useful for a Statistician who is interested in using unsupervised learning techniques to find patterns and insights in data. Unsupervised learning is a powerful tool for exploring new data and generating hypotheses.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst helps organizations to improve their operations. This course may be useful for an Operations Research Analyst who is interested in using unsupervised learning techniques to identify inefficiencies and opportunities for improvement. Unsupervised learning is a powerful tool for finding patterns and insights in data, which can be used to improve the efficiency and profitability of operations.
Financial Analyst
Financial Analyst
A Financial Analyst helps organizations to make investment decisions. This course may be useful for a Financial Analyst who is interested in using unsupervised learning techniques to identify investment opportunities. Unsupervised learning is a powerful tool for finding patterns and insights in data, which can be used to make more informed investment decisions.
Marketing Analyst
Marketing Analyst
A Marketing Analyst helps organizations to understand their customers and develop marketing campaigns. This course may be useful for a Marketing Analyst who is interested in using unsupervised learning techniques to segment customers and identify target markets. Unsupervised learning is a powerful tool for finding patterns and insights in data, which can be used to develop marketing campaigns that are more effective.
Product Manager
Product Manager
A Product Manager manages the development and launch of new products. This course may be useful for a Product Manager who is interested in using unsupervised learning techniques to understand customer needs and preferences. Unsupervised learning is a powerful tool for finding patterns and insights in data, which can be used to develop products that meet the needs of customers.
Business Analyst
Business Analyst
A Business Analyst helps organizations to improve their business processes. This course may be useful for a Business Analyst who is interested in using unsupervised learning techniques to identify inefficiencies and opportunities for improvement. Unsupervised learning is a powerful tool for finding patterns and insights in data, which can be used to improve the efficiency and profitability of businesses.
Database Administrator
Database Administrator
A Database Administrator manages and maintains databases. This course may be useful for a Database Administrator who is interested in using unsupervised learning techniques to improve the performance and security of databases. Unsupervised learning is a powerful tool for finding patterns and insights in data, which can be used to identify and resolve performance and security issues.
Information Security Analyst
Information Security Analyst
An Information Security Analyst protects organizations from cyberattacks. This course may be useful for an Information Security Analyst who is interested in using unsupervised learning techniques to identify and prevent cyberattacks. Unsupervised learning is a powerful tool for finding patterns and insights in data, which can be used to detect and respond to cyberattacks more effectively.
Network Engineer
Network Engineer
A Network Engineer designs and maintains computer networks. This course may be useful for a Network Engineer who is interested in using unsupervised learning techniques to improve the performance and security of computer networks. Unsupervised learning is a powerful tool for finding patterns and insights in data, which can be used to identify and resolve performance and security issues.
Data Engineer
Data Engineer
A Data Engineer designs and builds data pipelines. This course may be useful for a Data Engineer who is interested in using unsupervised learning techniques to improve the efficiency and accuracy of data pipelines. Unsupervised learning is a powerful tool for finding patterns and insights in data, which can be used to improve the performance of data pipelines.
For more career information including salaries, visit:
OpenCourser.com/course/dnibbe/udacity
Reading list
We've selected 20 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning: Unsupervised Learning.
Comprehensive introduction to unsupervised learning. It covers a wide range of topics, including clustering, dimensionality reduction, and feature selection.
Save
Offers an in-depth exploration of clustering algorithms. It is an excellent resource for learners who want to gain a comprehensive understanding of this important unsupervised learning technique.
Comprehensive introduction to dimensionality reduction. It covers a wide range of topics, including principal component analysis, linear discriminant analysis, and manifold learning.
Provides a comprehensive overview of information theory and its applications in machine learning. It valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of unsupervised learning.
Provides a comprehensive overview of machine learning techniques for data streams. It valuable resource for learners who want to gain a deeper understanding of this important and emerging field.
Provides a comprehensive overview of statistical learning techniques for sparse data. It valuable resource for learners who want to gain a deeper understanding of this important and emerging field.
Save
Classic introduction to machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Comprehensive introduction to pattern recognition and machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Provides a comprehensive overview of Bayesian data analysis techniques. It valuable resource for learners who want to gain a deeper understanding of this important and emerging field.
Offers an in-depth exploration of probabilistic graphical models. It is an excellent resource for learners who want to gain a comprehensive understanding of this important and emerging field.
Practical introduction to machine learning for hackers. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Practical introduction to machine learning using Python. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Offers an in-depth exploration of machine learning from a probabilistic perspective. It is an excellent resource for learners who want to gain a comprehensive understanding of this important and emerging field.
Practical introduction to machine learning for developers. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Practical introduction to machine learning using Python. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
Save
Provides a comprehensive overview of deep learning techniques. It valuable resource for learners who want to gain a deeper understanding of this important and emerging field.
Comprehensive introduction to natural language processing. It covers a wide range of topics, including text classification, text clustering, and sentiment analysis.
Offers an in-depth exploration of reinforcement learning techniques. It is an excellent resource for learners who want to gain a comprehensive understanding of this important and emerging field.
Provides a comprehensive overview of natural language processing techniques using Python. It valuable resource for learners who want to gain a deeper understanding of this important and emerging field.
Save
Offers an in-depth exploration of computer vision techniques. It is an excellent resource for learners who want to gain a comprehensive understanding of this important and emerging field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/dnibbe/udacity
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Via
Udacity
Instructors
Charles Isbell
Michael Littman
Pushkar Kolhe
Language
English
Good to know
Explores unsupervised learning, which is increasingly relevant to data mining and machine learning
Led by experienced instructors Charles Isbell, Michael Littman, and Pushkar Kolhe
Offers hands-on labs and interactive materials
Taught by Charles Isbell, who developed the first known robot to learn to walk with reinforcement learning
Taught by Michael Littman, who developed the first known computer agent which mastered game of Go
Provides a strong foundation for beginners in unsupervised learning
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